#librispeech — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #librispeech, aggregated by home.social.
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PVAD: как научить ИИ слышать нужного человека в шумной комнате
Представьте переговорную, где одновременно говорят несколько человек, а системе нужно понять, когда говорит целевой спикер, и выделить именно его голос. Задача нетривиальная, но именно её нам и нужно было решить. Над этим мы с коллегами работали в проекте PVAD (Personal Voice Activity Detection) — технологии для определения того, когда в общем аудиопотоке говорит конкретный человек. Короткий простой обзор на один из кейсов нашей команды.
https://habr.com/ru/articles/1069410/
#машинное_обучение #выделение_голоса #шумоподавление #Demucs #xvector #ResNet #PyTorch #TensorFlow #LibriSpeech #VoxCeleb2
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Delighted to be able to publicise a paper that was presented at the @ALTAnlp 2023 Workshop at the end of last year, co-authored with my #PhD supervisor, Associate Professor @eltwilliams, and written as part of my research at #ANU School of Cybernetics.
Titled "Right the docs: Characterising voice dataset documentation practices used in machine learning", it combines both exploratory interviews and documentation analysis to characterise how large voice datasets - e.g. #LibriSpeech, @mozilla's #CommonVoice, and several others, document their #metadata.
Unsurprisingly, it finds that the #dataset #documentation practices seen currently do not meet the needs of the #ML practitioners who use these datasets.
We show, once again, in the words of Nithya Sambasivan - "everyone wants to do the model work, but nobody wants to do the data work" ...
https://aclanthology.org/2023.alta-1.6/
Citation:
Reid, K., Williams, E.T., 2023. Right the docs: Characterising voice dataset documentation practices used in machine learning, in: Muresan, S., Chen, V., Casey, K., David, V., Nina, D., Koji, I., Erik, E., Stefan, U. (Eds.), Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association. Association for Computational Linguistics, Melbourne, Australia, pp. 51–66.
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Delighted to be able to publicise a paper that was presented at the @ALTAnlp 2023 Workshop at the end of last year, co-authored with my #PhD supervisor, Associate Professor @eltwilliams, and written as part of my research at #ANU School of Cybernetics.
Titled "Right the docs: Characterising voice dataset documentation practices used in machine learning", it combines both exploratory interviews and documentation analysis to characterise how large voice datasets - e.g. #LibriSpeech, @mozilla's #CommonVoice, and several others, document their #metadata.
Unsurprisingly, it finds that the #dataset #documentation practices seen currently do not meet the needs of the #ML practitioners who use these datasets.
We show, once again, in the words of Nithya Sambasivan - "everyone wants to do the model work, but nobody wants to do the data work" ...
https://aclanthology.org/2023.alta-1.6/
Citation:
Reid, K., Williams, E.T., 2023. Right the docs: Characterising voice dataset documentation practices used in machine learning, in: Muresan, S., Chen, V., Casey, K., David, V., Nina, D., Koji, I., Erik, E., Stefan, U. (Eds.), Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association. Association for Computational Linguistics, Melbourne, Australia, pp. 51–66.